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Yu-Jie Xiong

5 accepted papers

2026

Gradient-Direction-Aware Density Control for 3D Gaussian Splatting

ICLR 2026poster

The emergence of 3D Gaussian Splatting (3DGS) has significantly advanced Novel View Synthesis (NVS) through explicit scene representation, enabling real-time photorealistic rendering. However, existing approaches manifest two critical limitations in complex scenarios: (1) Over-reconstruction occurs…

Cited by 0SourcecodeScholar
2025

MSA2: Multi-task Framework with Structure-aware and Style-adaptive Character Representation for Open-set Chinese Text Recognition

ICCV 2025poster

Most existing methods regard open-set Chinese text recognition (CTR) as a single-task problem, primarily focusing on prototype learning of linguistic components or glyphs to identify unseen characters. In contrast, humans identify characters by integrating multiple perspectives, including linguistic…

2025

Parameter-Efficient Fine-Tuning of Large Language Models via Deconvolution in Subspace

COLING 2025main

This paper proposes a novel parameter-efficient fine-tuning method that combines the knowledge completion capability of deconvolution with the subspace learning ability, reducing the number of parameters required for fine-tuning by 8 times . Experimental results demonstrate that our method achieves…

2025

Sugar-Coated Poison: Benign Generation Unlocks Jailbreaking

EMNLP 2025

With the increasingly deep integration of large language models (LLMs) across diverse domains, the effectiveness of their safety mechanisms is encountering severe challenges. Currently, jailbreak attacks based on prompt engineering, which induce models to generate potentially harmful content, have b

2025

Why 1 + 1 < 1 in Visual Token Pruning: Beyond Naive Integration via Multi-Objective Balanced Covering

NeurIPS 2025poster

Existing visual token pruning methods target prompt alignment and visual preservation with static strategies, overlooking the varying relative importance of these objectives across tasks, which leads to inconsistent performance. To address this, we derive the first closed-form error bound for visual…

Cited by 0SourceScholar